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The elements of algorithms

65 pointsby october_skyover 5 years ago

4 comments

gus_massaover 5 years ago
This is very far from a rediscovery. The program just rearrange the elements with some criteria in the plane.<p>The important part of the periodic table was that it is a regular arrangement. The elements in the same column and rows share some properties, you can somewhat guess the properties of an element looking at the properties of the nearby elements.<p>For example, all the elements in the first column are alkaline, they form salts with chloride like XCl. The solubility, density, ... of these salts are somewhat related. Some columns have a clear relation, some columns have more messy relations.<p>The relation in each row is less useful, only atomic weight, radius and electronegativity, but there may be some special cases an exceptions.<p>A clear grid arrangement by Mendeleev has two important properties:<p>* He first ordered the elements by atomic weight, but it has a few weird cases, so he decided to rearrange them. This rearrangement was later explained when the internal structure of the atoms was understood.<p>* Some slots in the grid were missing, so he predicted some new elements and their properties. These elements were discover later, with properties similar to the predicted.<p>It&#x27;s not very clear how much insight these new representation provide. To make the task more clear: If these algorithm use only the data available to Mendeleev, can they discover germanium and gallium?
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hyperion2010over 5 years ago
Darn and here I was hoping that it did it by reading a bunch of chemistry papers from the 19th century.
sytelusover 5 years ago
TLDR; To figure out property of chemical composition of one specific form there is a method called DFT which is expensive. There are 10^6 combinations for this molecule composition from elements. So they train ridge-regression model to predict it instead of DFT and find some interesting compositions.<p>I wonder if it is possible to find much more general molecules that might have interesting properties through ML.<p>Original paper: <a href="https:&#x2F;&#x2F;journals.aps.org&#x2F;prl&#x2F;pdf&#x2F;10.1103&#x2F;PhysRevLett.117.135502" rel="nofollow">https:&#x2F;&#x2F;journals.aps.org&#x2F;prl&#x2F;pdf&#x2F;10.1103&#x2F;PhysRevLett.117.135...</a>
throwaway-1436over 5 years ago
I didn&#x27;t follow. If you take a high dimensional data set and project it are you not going to find patterns? In fact, let me ask it differently. Are there any projections that don&#x27;t find patterns?
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